





import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCamelCase 					=      logging.get_logger(__name__)




class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def __init__(				self     :int				,					*__A     :int				,					**__A     :Any		) -> None:



									"""simple docstring"""



									warnings.warn(
									    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
									    """ use DonutImageProcessor instead."""				,					__A				,					)
									super().__init__(*__A				,					**__A		)

"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = SwinaSRConfig()

    if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        _UpperCAmelCase        = 4
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = 4_8
        _UpperCAmelCase        = """pixelshuffle_aux"""
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        _UpperCAmelCase        = [6, 6, 6, 6]
        _UpperCAmelCase        = 6_0
        _UpperCAmelCase        = [6, 6, 6, 6]
        _UpperCAmelCase        = """pixelshuffledirect"""
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = """nearest+conv"""
    elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 1_2_6
        _UpperCAmelCase        = 7
        _UpperCAmelCase        = 255.0
        _UpperCAmelCase        = """"""

    return config


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    if "patch_embed.proj" in name and "layers" not in name:
        _UpperCAmelCase        = name.replace("""patch_embed.proj"""       ,   """embeddings.patch_embeddings.projection"""   )
    if "patch_embed.norm" in name:
        _UpperCAmelCase        = name.replace("""patch_embed.norm"""       ,   """embeddings.patch_embeddings.layernorm"""   )
    if "layers" in name:
        _UpperCAmelCase        = name.replace("""layers"""       ,   """encoder.stages"""   )
    if "residual_group.blocks" in name:
        _UpperCAmelCase        = name.replace("""residual_group.blocks"""       ,   """layers"""   )
    if "attn.proj" in name:
        _UpperCAmelCase        = name.replace("""attn.proj"""       ,   """attention.output.dense"""   )
    if "attn" in name:
        _UpperCAmelCase        = name.replace("""attn"""       ,   """attention.self"""   )
    if "norm1" in name:
        _UpperCAmelCase        = name.replace("""norm1"""       ,   """layernorm_before"""   )
    if "norm2" in name:
        _UpperCAmelCase        = name.replace("""norm2"""       ,   """layernorm_after"""   )
    if "mlp.fc1" in name:
        _UpperCAmelCase        = name.replace("""mlp.fc1"""       ,   """intermediate.dense"""   )
    if "mlp.fc2" in name:
        _UpperCAmelCase        = name.replace("""mlp.fc2"""       ,   """output.dense"""   )
    if "q_bias" in name:
        _UpperCAmelCase        = name.replace("""q_bias"""       ,   """query.bias"""   )
    if "k_bias" in name:
        _UpperCAmelCase        = name.replace("""k_bias"""       ,   """key.bias"""   )
    if "v_bias" in name:
        _UpperCAmelCase        = name.replace("""v_bias"""       ,   """value.bias"""   )
    if "cpb_mlp" in name:
        _UpperCAmelCase        = name.replace("""cpb_mlp"""       ,   """continuous_position_bias_mlp"""   )
    if "patch_embed.proj" in name:
        _UpperCAmelCase        = name.replace("""patch_embed.proj"""       ,   """patch_embed.projection"""   )

    if name == "norm.weight":
        _UpperCAmelCase        = """layernorm.weight"""
    if name == "norm.bias":
        _UpperCAmelCase        = """layernorm.bias"""

    if "conv_first" in name:
        _UpperCAmelCase        = name.replace("""conv_first"""       ,   """first_convolution"""   )

    if (
        "upsample" in name
        or "conv_before_upsample" in name
        or "conv_bicubic" in name
        or "conv_up" in name
        or "conv_hr" in name
        or "conv_last" in name
        or "aux" in name
    ):
        # heads
        if "conv_last" in name:
            _UpperCAmelCase        = name.replace("""conv_last"""       ,   """final_convolution"""   )
        if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
            if "conv_before_upsample.0" in name:
                _UpperCAmelCase        = name.replace("""conv_before_upsample.0"""       ,   """conv_before_upsample"""   )
            if "upsample.0" in name:
                _UpperCAmelCase        = name.replace("""upsample.0"""       ,   """upsample.convolution_0"""   )
            if "upsample.2" in name:
                _UpperCAmelCase        = name.replace("""upsample.2"""       ,   """upsample.convolution_1"""   )
            _UpperCAmelCase        = """upsample.""" + name
        elif config.upsampler == "pixelshuffledirect":
            _UpperCAmelCase        = name.replace("""upsample.0.weight"""       ,   """upsample.conv.weight"""   )
            _UpperCAmelCase        = name.replace("""upsample.0.bias"""       ,   """upsample.conv.bias"""   )
        else:
            pass
    else:
        _UpperCAmelCase        = """swin2sr.""" + name

    return name


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        _UpperCAmelCase        = orig_state_dict.pop(_A   )

        if "qkv" in key:
            _UpperCAmelCase        = key.split("""."""   )
            _UpperCAmelCase        = int(key_split[1]   )
            _UpperCAmelCase        = int(key_split[4]   )
            _UpperCAmelCase        = config.embed_dim

            if "weight" in key:
                _UpperCAmelCase        = val[:dim, :]
                _UpperCAmelCase        = val[dim : dim * 2, :]
                _UpperCAmelCase        = val[-dim:, :]
            else:
                _UpperCAmelCase        = val[:dim]
                _UpperCAmelCase        = val[dim : dim * 2]
                _UpperCAmelCase        = val[-dim:]
            pass
        else:
            _UpperCAmelCase        = val

    return orig_state_dict


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = get_config(_A   )
    _UpperCAmelCase        = SwinaSRForImageSuperResolution(_A   )
    model.eval()

    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    _UpperCAmelCase        = convert_state_dict(_A       ,   _A   )
    _UpperCAmelCase     ,_UpperCAmelCase        = model.load_state_dict(_A       ,   strict=_A   )

    if len(_A   ) > 0:
        raise ValueError("""Missing keys when converting: {}""".format(_A   )   )
    for key in unexpected_keys:
        if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
            raise ValueError(F"""Unexpected key {key} in state_dict"""   )

    # verify values
    _UpperCAmelCase        = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
    _UpperCAmelCase        = Image.open(requests.get(_A       ,   stream=_A   ).raw   ).convert("""RGB"""   )
    _UpperCAmelCase        = SwinaSRImageProcessor()
    # pixel_values = processor(image, return_tensors="pt").pixel_values

    _UpperCAmelCase        = 1_2_6 if """Jpeg""" in checkpoint_url else 2_5_6
    _UpperCAmelCase        = Compose(
        [
            Resize((image_size, image_size)   ),
            ToTensor(),
            Normalize(mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   ),
        ]   )
    _UpperCAmelCase        = transforms(_A   ).unsqueeze(0   )

    if config.num_channels == 1:
        _UpperCAmelCase        = pixel_values[:, 0, :, :].unsqueeze(1   )

    _UpperCAmelCase        = model(_A   )

    # assert values
    if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 5_1_2, 5_1_2]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]   )
    elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]   )
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        # TODO values didn't match exactly here
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]   )
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 5_1_2, 5_1_2]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]   )
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]   )

    assert (
        outputs.reconstruction.shape == expected_shape
    ), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
    assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]       ,   _A       ,   atol=1e-3   )
    print("""Looks ok!"""   )

    _UpperCAmelCase        = {
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
            """swin2SR-classical-sr-x2-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
            """swin2SR-classical-sr-x4-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
            """swin2SR-compressed-sr-x4-48"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
            """swin2SR-lightweight-x2-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
            """swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
        ),
    }
    _UpperCAmelCase        = url_to_name[checkpoint_url]

    if pytorch_dump_folder_path is not None:
        print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
        model.save_pretrained(_A   )
        print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
        processor.save_pretrained(_A   )

    if push_to_hub:
        model.push_to_hub(F"""caidas/{model_name}"""   )
        processor.push_to_hub(F"""caidas/{model_name}"""   )


if __name__ == "__main__":
 a       :						Optional[int]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
     type=str,
     help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

 a       :						str       		=		parser.parse_args()
 convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)